What are the stages of artificial intelligence learning?
Artificial intelligence is an interdisciplinary and cross-field hybrid subject. Judging from future trends, the emergence of artificial intelligence has made people's lives better and more convenient. Many friends want to learn artificial intelligence, but in fact it seems that artificial intelligence is relatively complicated. There are so many, there is no way to start. We only need to start from the following 7 stages to give ourselves a clear learning idea.
Stage 1: Advanced Mathematics
The foundation of artificial intelligence, of which advanced mathematics is a must. Advanced mathematics includes data analysis, probability theory, linear algebra and matrices, convex optimization, etc. A good mathematical foundation will also help students better understand machine learning and deep learning in subsequent courses. At the same time, it is particularly important for AI research. For example, a large part of the intelligence in artificial intelligence is realized based on "probability theory".
Stage 2: Advanced applications of python
Advanced application of python is required. The python language plays an indispensable role in artificial intelligence. Machine learning is complex and often involves assembling workflows and pipelines, setting up data sources, and splitting between on-premises and cloud deployments. Python can better handle the data pipeline. It makes it easier for us to learn machine learning.
Phase Three: Machine Learning
Get started with machine learning. Machine learning involves many complex algorithms, which are used to analyze and learn from data. Then make judgments about the reality of the situation and respond to it. For example, speech recognition obtains speech data from external users, then performs algorithm analysis, and finally recognizes it as text and displays it on your device.
Stage 4: Data Mining
Conduct data mining to collect and analyze data. As the name suggests, data mining is to mine data, collect and analyze data through algorithms, and simulate people's original learning form. Data mining involves a lot of knowledge, such as database technology, machine learning, statistics, data warehouse technology, etc.
Stage 5: Deep Learning
Deep learning. Deep learning is a branch of machine learning and a technology for realizing machine learning. At the same time, deep learning also brings many practical applications to machine learning. Explain deep learning related algorithms from TensorFlow, BP neural network, overview of deep learning, CNN convolutional neural network, recursive neural network, automatic encoding machine, sequence-to-sequence network, generative adversarial network, twin network, small sample learning technology, etc.
Stage Six: Natural Language
Natural language processing. Natural language processing has always been an important direction in the fields of computer science and artificial intelligence. Natural language is languages such as Chinese and English. This type of language has always been the exclusive prerogative of us humans. Natural language processing at this stage is to enable the machine to understand and process natural language.
Stage Seven: Image Processing
Image processing is a method and technology that uses computers to obtain images and perform processing such as removing noise, enhancing, restoring, segmenting, and extracting features. It has been widely used in various fields.
Learning artificial intelligence is like a never-ending marathon. It is a long-term process and has no bottom. As one of the majority of people, we should proceed steadily, combine the theoretical framework with actual projects, and combine artificial intelligence with Learn this course well. As long as you work hard, a bright and well-paid future is waiting for you!